We now turn to convex optimization methods for solving (REF ).
Recall that \(H(t) = t \cdot {\rm log}(t) - t\)  and hence \(H^*(s) = {\rm exp}(s)\)  in
the dual formulation. We can solve (REF ) using
coordinate ascent, i.e. by iteratively optimizing each variable \(p_i\)  in (REF ) in a cyclic order.
In statistics, this is known as iterative proportional scaling (IPS, see [1]}, [2]}).
This method converges linearly [3]}.
Randomized iterations over the \(p_i\)  can further improve the performance. When each one-dimensional
optimization is computationally cheap, this method is particularly interesting.
